Discover @kpradeepkumarreddy Open Source projects

K Pradeep Kumar Reddy (@kpradeepkumarreddy)

kpradeepkumarreddy

Stars
34
Global Rank 448,285 (Top 16 %)
Followers 15
Following 86
Registered almost 9 years ago
Most used languages

Java
36.4 %

Kotlin
22.7 %

Jupyter Notebook
18.2 %

JavaScript
13.6 %

C++
4.5 %

QML 4.5 %
Location 🇮🇳 India
Country Total Rank 601
Country Ranking

QML 34

Kotlin
1,338

Java
4,365

Jupyter Notebook
9,055

todo-list

Vanilla Javascript TODO List App

HackerEarthCodingChallenges

Coding Challenges from HackerEarth website

GeekTrustFindingFalconeChallenge

This is a frontend coding challenge from geektrust.in website

Sorting-Algorithms-Benchmarking-App

Java program which allows the user to choose array size, sorting algorithm, worst, best or average case complexity. Based on the input, program will display the time consumption of the selected sorting algorithm

custom-gallery

Custom gallery android app. We can select multiple images and display them in our current activity in grid layout fashion

DaggerBirdShooter

jsons

sample jsons for testing

GuessTheWord

kpradeepkumarreddy

OmniCurisTestQuizApp

omni curis test quiz app

QtQml-ContactsApp

Read contacts from Android and display in QML and also listen to contacts changes and update QML side accordingly.

RecipeCalculator

Recipe Calculator App using Flutter

EmojiPicker

QtQmlAndroidContactsApp

tesla-clone-react-native

WeatherForecast

SimplilearnFullStackPhase1Project

PaginationInRecyclerView

pagination in recycler view

BlueRoseAndroidAssignment

BlueRose Technologies android assignment

SongsList

songs list in json file

QtQml-MediaPlayer

Qt Qml Media Player

GeekTrustGoldenCrownChallenge

This is one of backend coding challenges from geektrust.in website.

Investment-analysis-for-an-asset-management-company

Asset management company wants to understand the global trends in investments so that they can take the investment decisions effectively.

Jupyter Notebook

multiple-linear-regression

A Chinese automobile company Geely Auto aspires to enter the US market by setting up their manufacturing unit there and producing cars locally to give competition to their US and European counterparts. They have contracted an automobile consulting company to understand the factors on which the pricing of cars depends. Specifically, they want to understand the factors affecting the pricing of cars in the American market, since those may be very different from the Chinese market. The company wants to know: Which variables are significant in predicting the price of a car How well those variables describe the price of a car Based on various market surveys, the consulting firm has gathered a large dataset of different types of cars across the Americal market.

Jupyter Notebook

lending-club-case-study

A consumer finance company lends various types of loans to urban customers. When the company receives a loan application, the company has to make a decision for loan approval based on the applicant’s profile. Two types of risks are associated with the bank’s decision: If the applicant is likely to repay the loan, then not approving the loan results in a loss of business to the company If the applicant is not likely to repay the loan, i.e. he/she is likely to default, then approving the loan may lead to a financial loss for the company The data given below contains the information about past loan applicants and whether they ‘defaulted’ or not. The aim is to identify patterns which indicate if a person is likely to default, which may be used for taking actions such as denying the loan, reducing the amount of loan, lending (to risky applicants) at a higher interest rate, etc.

Jupyter Notebook

telecom-churn-case-study

Business Problem Overview In the telecom industry, customers are able to choose from multiple service providers and actively switch from one operator to another. In this highly competitive market, the telecommunications industry experiences an average of 15-25% annual churn rate. Given the fact that it costs 5-10 times more to acquire a new customer than to retain an existing one, customer retention has now become even more important than customer acquisition. For many incumbent operators, retaining high profitable customers is the number one business goal. To reduce customer churn, telecom companies need to predict which customers are at high risk of churn. In this project, you will analyse customer-level data of a leading telecom firm, build predictive models to identify customers at high risk of churn and identify the main indicators of churn.

Jupyter Notebook